Advances and perspectives of mechanomyography

INTRODUCTION: The evaluation of muscular tissue condition can be accomplished with mechanomyography (MMG), a technique that registers intramuscular mechanical waves produced during a fiber's contraction and stretching that are sensed or interfaced on the skin surface. OBJECTIVE: Considering the scope of MMG measurements and recent advances involving the technique, the goal of this paper is to discuss mechanomyography updates and discuss its applications and potential future applications. METHODS: Forty-three MMG studies were published between the years of 1987 and 2013. RESULTS: MMG sensors are developed with different technologies such as condenser microphones, accelerometers, laser-based instruments, etc. Experimental protocols that are described in scientific publications typically investigated the condition of the vastus lateralis muscle and used sensors built with accelerometers, third and fourth order Butterworth filters, 5-100Hz frequency bandpass, signal analysis using Root Mean Square (RMS) (temporal), Median Frequency (MDF) and Mean Power Frequency (MPF) (spectral) features, with epochs of 1 s. CONCLUSION: Mechanomyographic responses obtained in isometric contractions differ from those observed during dynamic contractions in both passive and functional electrical stimulation evoked movements. In the near future, MMG features applied to biofeedback closed-loop systems will help people with disabilities, such as spinal cord injury or limb amputation because they may improve both neural and myoelectric prosthetic control. Muscular tissue assessment is a new application area enabled by MMG; it can be useful in evaluating the muscular tonus in anesthetic blockade or in pathologies such as myotonic dystrophy, chronic obstructive pulmonary disease, and disorders including dysphagia, myalgia and spastic hypertonia. New research becomes necessary to improve the efficiency of MMG systems and increase their application in rehabilitation, clinical and other health areas304384401CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQFINANCIADORA DE ESTUDOS E PROJETOS - FINEPsem informaçã

The use of surface electromyography in muscle fatigue assessments–a review

The developments in physiological studies have established the importance of muscle fatigue estimation in various aspects including neurophysiological and medical research, rehabilitation, ergonomics, sports injuries and human-computer interaction. Surface electromyography signals are commonly used in muscle fatigue assessment. Techniques of surface EMG signal processing used to quantify muscle fatigue are not only based on time domain and frequency domain, but also on time–frequency domain. The developments of different signal analysis to extract different indices for muscle fatigue assessments are reviewed in this paper. Several indices in time, frequency, and time-frequency representations for muscle fatigue assessments have been identified. However the sensitivity of those indices needs to be investigated. Minimizing this issue becomes the objective of the recent research in muscle fatigue assessments

Temporal spectral approach to surface electromyography based fatigue classification of biceps brachii during dynamic contraction

Muscle fatigue is defined as a reduction in muscle’s ability to contract and produce force due to prolonged submaximal exercise. Since fatigue is not a physical variable, fatigue indices are commonly used to detect and monitor muscle fatigue development. One suggested approach to quantitative measurement of muscle fatigue is based on surface electromyography (sEMG) signal. Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) are commonly used techniques to obtain time-frequency representation of sEMG signals. However, S Transform (ST) technique has not been applied much to physiological signals. No found literature has used ST technique to extract muscle fatigue indices. Thus, this study intends to determine the feasibility of using ST technique to extract muscle fatigue indices from sEMG signal. Thirty college students with no illness history were randomly selected to perform bicep curl activities for 130 seconds while holding a 2 kg dumbbell. Using the three time-frequency techniques (STFT, CWT, and ST), four commonly extracted muscle fatigue indices (Instantaneous Energy Distribution (IED), Instantaneous Mean Frequency (IMNF), Instantaneous Frequency Variance (IFV) and Instantaneous Normalize Spectral Moment (INSM)) were extracted from the acquired biceps sEMG signals. Indices from fatigue signals were found to be significantly different (p-value < 0.05) from the non-fatigue signals. Based on the Normalization of Root Mean Square Error (NRMSE) and Relative Error, ST technique was found to produce less error than STFT and CWT techniques in extracting muscle fatigue indices. Through the use of 3-fold cross validation procedure and with the help of Support Vector Machine (SVM) classifier, IMNF-IED-IFV was selected as the best feature combination for classifying the two phases of muscle fatigue with consistent classification performance (accuracy, sensitivity and specificity) of 80%. Therefore, this study concludes that ST processing technique is feasible to be applied to sEMG signals for extracting screening or monitoring measures of muscle fatigue with a good degree of certainty

Choice Of Mechanomyography Sensors For Diverse Types Of Muscle Activities

Skeletal muscles contribute to the movement produced in the human body.They are therefore of vital importance for the study of muscles in various applications of movement including exercise,sports,prosthesis,rehabilitation,etc. The movement produced by skeletal muscles can be analyzed through various techniques like mechanomyography (MMG) and electromyography (EMG).MMG is a novel technique to assess skeletal muscle function through the oscillations produced during muscle contractions.MMG advocates well for its reliability,performance,and ease in application to other presently used techniques.MMG employs several types of sensors to observe vibrations in skeletal muscles.These sensors vary widely from application to type of movement and muscle.This review provides a comprehensive chunk of information on MMG sensor selection according to its placement,muscle function and condition,and limb movement and application. Recommendations for the choice of MMG sensor are given through extensive literature search over here

Feasibility of a surface electromyography-based compression garment for monitoring internal player load in professional basketball

The psychophysiological demands placed on professional athletes nowadays is greater than ever. In fact, professional basketball players can compete up to three time per week in addition to frequent and regular training sessions. Thus, adequately prescribing and monitoring athletes’ loads is important to maintain player well-being, reduce fatigue while optimising performance. Therefore, sport science research is saturated with different internal and external load monitoring approaches to help teams achieve these goals. Expansion of the global wearable technology market in sport is ever growing as practitioners seek a competitive advantage to their competitors. One such technology which has clinically and extensively been used for decades but has entered a new era into the wearable technology field in sport is surface electromyography (sEMG). However, little research reports on this technology in sport and the internal load metrics which representative companies claim it can report. The purpose of this doctoral thesis was to comprehensively examine internal load experienced by professional basketball players in the British Basketball League (BBL), while investigating a wearable sEMG technology for reporting a novel sEMG-based internal load metric (“Training Load”) during controlled lab-based exercise protocols, as well as determine the feasibility of the wearable sEMG-based internal load monitoring system in the professional basketball environment. The first observational study assessed the internal load experienced by professional basketball players during an entire season in the BBL. The research used the session-rating of perceived exertion (sRPE) method for quantifying load in professional basketball players following training sessions and competition. Results show that players experience greater Weekly Load (training only) during preseason compared to the in-season phase. Weekly Load is greater in 1-game weeks compared to 2-game weeks, while Total Weekly Load (training and competition) is higher during 2-game weeks compared to 1-game weeks. In addition, starting players experience a moderately higher Total Weekly Load compared to bench players, yet playing status did not result in differences in Weekly Load. The results show variances in internal load depending on weekly game fixtures, training schedules and phases of the season. While the sRPE method provides a valid global measurement of the training session or competition, the nature of retrieving RPE’s from players by asking a question prevents deeper investigation of internal load from specific phases of play. The second investigative study explores the possibility of using a novel wearable sEMG garment for capturing internal load (Training Load). The research investigated the sEMG derived Training Load during a 3-speed treadmill test and its relationship with oxygen consumption (V̇O2) during an exhaustive ramp incremental running treadmill test to determine maximal oxygen uptake (V̇O2max). Findings demonstrate sEMG-derived Training Load is a sensitive measure in detecting small changes in work rate during dynamic exercise, and while a moderate positive correlation between %V̇O2 max is shown, 80% of participants’ Training Loads show a very strong positive correlation at the individual level. The findings conclude that wearable sEMG technology may provide an alternative and new approach to capturing players internal load during sport and dynamic, whole-body exercise. The third study investigates the feasibility, practicality, and acceptability of wearable sEMG technology in the professional basketball environment. Results show a high acceptance rate (seventy-five percent) of the sEMG technology amongst professional basketball players, who report they would use the wearable sEMG technology again during team basketball training. A minority of players (twenty-five percent) report they would not use the wearable sEMG technology again due to negative experiences such as, comfortability issues and perceived negative effects on performance. While the wearable sEMG technology is relatively feasible in the environment, a few practical implications are considered important for coaches to understand before use. In particular, the time taken for downloading data to report to coaching staff or players takes longer than other load monitoring systems, such as GPS. In addition, the technology is more suited to the professional environment where a kit manager takes care of the handling procedures associated with the shorts. Lastly, the Core unit attached to the shorts can interrupt training practice. The current thesis contributes original research to the field of wearable sEMG for monitoring internal load. Findings provide important implications for practitioners endeavouring to use wearable sEMG in a professional sport context or research to further extent. Most research in basketball is conducted internationally, within Europe and America. The thesis is one of the first studies to identify internal loads in professional male basketball players within the United Kingdom. The thesis was the first to investigate an sEMG-derived Training Load during specific running tests. Lastly, the thesis was the first to assess professional athletes’ perceptions on wearable sEMG technology, highlighting reasons for and against using the technology

NEUROMUSCULAR BIOMECHANICS IN SPORTS

The purpose of this review was to highlight some of the more controversial aspects of neuromuscular biomechanics of sport. Neuromuscular techniques and interpretations of findings have been presented with the intent of showing the promise that research in this area has as well as the present limlations. In each case. I have tried to advocate an opinion by showing results from my own laboratory as well as to cite alternate opinions from top level laboratories elsewhere

Combined influence of forearm orientation and muscular contraction on EMG pattern recognition

AbstractThe performance of intelligent electromyogram (EMG)-driven prostheses, functioning as artificial alternatives to missing limbs, is influenced by several dynamic factors including: electrode position shift, varying muscle contraction level, forearm orientation, and limb position. The impact of these factors on EMG pattern recognition has been previously studied in isolation, with the combined effect of these factors being understudied. However, it is likely that a combination of these factors influences the accuracy. We investigated the combined effect of two dynamic factors, namely, forearm orientation and muscle contraction levels, on the generalizability of the EMG pattern recognition. A number of recent time- and frequency-domain EMG features were utilized to study the EMG classification accuracy. Twelve intact-limbed and one bilateral transradial (below-elbow) amputee subject were recruited. They performed six classes of wrist and hand movements at three muscular contraction levels with three forearm orientations (nine conditions). Results indicate that a classifier trained by features that quantify the angle, rather than amplitude, of the muscle activation patterns perform better than other feature sets across different contraction levels and forearm orientations. In addition, a classifier trained with the EMG signals collected at multiple forearm orientations with medium muscular contractions can generalize well and achieve classification accuracies of up to 91%. Furthermore, inclusion of an accelerometer to monitor wrist movement further improved the EMG classification accuracy. The results indicate that the proposed methodology has the potential to improve robustness of myoelectric pattern recognition